A non-linear mathematical model for rain making from water
vapor in the atmosphere is proposed and analyzed. The model considers the
process of artificial rain by introducing two kinds of aerosol particles
conducive to nucleation of cloud droplets and formation of rain drops. The
model analysis shows that, for uninterrupted rain, the water vapor in the
atmosphere must be formed continuously with the required rate of rainfall. It
is shown further that the intensity of rainfall increases as the concentrations
of externally introduced aerosols, as well as the density of water vapor in the
atmosphere, increase. Numerical simulation is also performed to see the effect
of various parameters on the process of artificial rain making leading to
rainfall.

3.

Rain intensity forecast using Artificial Neural Networks in
Athens, Greece Original Research Article

Atmospheric Research, Volume 119, January 2013, Pages
153-160

P.T. Nastos, K.P. Moustris, I.K. Larissi, A.G. Paliatsos

Abstrak:

The forecast of extreme weather events become imperative due
to the emerging climate change and possible adverse effects in humans. The objective
of this study is to construct predictive models in order to forecast rain
intensity (mm/day) in Athens, Greece, using Artificial Neural Networks (ANN)
models. The ANNs outcomes concern the projected mean, maximum and minimum
monthly rain intensity for the next four consecutive months in Athens. The
meteorological data used to estimate the rain intensity, were the monthly rain
totals (mm) and the respective rain days, which were acquired from the National
Observatory of Athens, for a 111-year period (1899–2009). The results of the
developed and applied ANN models showed a fairly reliable forecast of the rain
intensity for the next four months. For the evaluation of the results and the
ability of the developed prognostic models, appropriate statistical indices
were taken into consideration. In general, the predicted rain intensity
compared with the corresponding observed one seemed to be in a very good
agreement at a statistical significance level of p < 0.01.

4.

An Artificial Neural Network based approach for estimation
of rain intensity from spectral moments of a Doppler Weather Radar Original
Research Article

By using a Doppler Weather Radar (DWR) at Shriharikota
(13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is
proposed to improve the accuracy of rain intensity estimation. Three spectral
moments of a Doppler spectra are utilized as an input data to an ANN. Rain
intensity, as measured by the tipping bucket rain gauges around the DWR
station, are considered as a target values for the given inputs. Rain intensity
as estimated by the developed ANN model is validated by the rain gauges
measurements. With the help of a developed technique, reasonable improvement in
the estimation of rain intensity is observed. By using the developed technique,
root mean square error and bias are reduced in the range of 34–18% and 17–3%
respectively, compared to Z–R approach.

5.

Artificial rain and cold wind act as stressors to captive
molting and non-molting European starlings (Sturnus vulgaris) Original Research
Article

Free-roaming animals continually cope with changes in
their environment. One of the most unpredictable environmental phenomena is
weather. Being able to respond to weather appropriately is crucial as it can be
a threat to survival. The stress response, consisting of increases in heart
rate and release of glucocorticoids, is an important mechanism by which animals
cope with stressors. This study examined behavioral, heart rate, and
corticosterone responses of captive European starlings (Sturnus vulgaris)
to two aspects of weather mimicked under controlled conditions, a subtle
(3 °C) decrease in temperature and a short, mild bout of rain. Both
decreased temperature and exposure to rain elicited increases in heart rate and
corticosterone in non-molting starlings. Molt is an important life history
stage in birds that affects feather cover and may require a different response
to weather-related stressors. We repeated the experiment in molting starlings
and found increases in heart rate in response to rain and cold wind. However,
the hypothalamic–pituitary–adrenal (HPA)-axis was suppressed during molt, as
molting starlings did not increase corticosterone release in response to either
stimulus. These data suggest these stimuli induce increased allostatic load in
starlings, and that animals may adjust their response depending on the
life-history stage.